As we have mentioned in the introduction section, more than half of the baseline model's errors on the AIDA-A dataset are type errors. Type errors are error casesWe discard the error cases due to candidate generation problem (i.e., gold entities that do not appear in mentions' candidate list) which cover 2.98% mentions of AIDA-A dataset. where (1) the predicted entity's type is different from the golden entity's type; (2) contextual cue predictive of the type of the mention exists; (3) errors are not due to annotation errors. By doing so, we collect 185 type error cases which cover 57.45% of all (322) error cases. This indicates that [1]} ganea2017deep produces many type errors due to its inability to consider the entity type information in mention context. By integrating the BERT-based entity similarity, our proposed model can correct 124 out of 185 (67.03%) type error cases of the baseline model which demonstrates that we correct more than two third of the type errors produced by the baseline. We have further examined and categorized the remaining 61 type error cases into three categories: (i) Due to prior: golden entities with very low \(\hat{p}(e|m_i)\)  prior, (ii) Due to global: both the local context score and prior score support predicting the golden entity, but the overall score supports predicting incorrect entity due to global modeling, (iii) Due to local context: the local context score misleads the model predicting the wrong entity, this is potentially due to the mention context can be misleading, e.g. a document discussing cricket will favor resolving the mention “Australian" in context “impressed by the positive influence of Australian coach Dave Gilbert" to the entity Australia_national_cricket_team instead of the gold entity Australia.
